Jorge G. Barbosa

List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table

Efficient application scheduling algorithms are important for obtaining high performance in heterogeneous computing systems. In this paper, we present a novel list-based scheduling algorithm called Predict Earliest Finish Time (PEFT) for heterogeneous computing systems. The algorithm has the same time complexity as the state-of-the-art algorithm for the same purpose, that is, O(v2.p) for v tasks and p processors, but offers significant makespan improvements by introducing a look-ahead feature without increasing the time complexity associated with computation of an optimistic cost table (OCT). The calculated value is an optimistic cost because processor availability is not considered in the computation. Our algorithm is only based on an OCT that is used to rank tasks and for processor selection. The analysis and experiments based on randomly generated graphs with various characteristics and graphs of real-world applications show that the PEFT algorithm outperforms the state-of-the-art list-based algorithms for heterogeneous systems in terms of schedule length ratio, efficiency, and frequency of best results.

Linear algebra algorithms in a heterogeneous cluster of personal computers

Cluster computing is presently a major research area, mostly for high performance computing. The work presented refers to the application of cluster computing in a small scale where a virtual machine is composed of a small number of off-the-self-personal computers connected by a low cost network. A methodology to determine the optimal number of processors to be used in a computation is presented as well as the speedup results obtained for the matrix-matrix multiplication and for the symmetric QR algorithm for eigenvector computation which are significant building blocks for applications in the target image processing and analysis domain. The load balancing strategy is also addressed.

A Budget Constrained Scheduling Algorithm for Workflow Applications

Service-oriented computing has enabled a new method of service provisioning based on utility computing models, in which users consume services based on their Quality of Service (QoS) requirements. In such pay-per-use models, users are charged for services based on their usage and on the fulfilment of QoS constraints; execution time and cost are two common QoS requirements. Therefore, to produce effective scheduling maps, service pricing must be considered while optimising execution performance. In this paper, we propose a Heterogeneous Budget Constrained Scheduling (HBCS) algorithm that guarantees an execution cost within the user’s specified budget and that minimises the execution time of the user’s application. The results presented show that our algorithm achieves lower makespans, with a guaranteed cost per application and with a lower time complexity than other budget-constrained state-of-the-art algorithms. The improvements are particularly high for more heterogeneous systems, in which a reduction of 30 % in execution time was achieved while maintaining the same budget level.

Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model

This paper proposes a novel method for fast 3D reconstructions of the scoliotic spine from two planar radiographs. The method uses a statistical model of the shape of the spine for computing the 3D reconstruction that best matches the user input (about 7 control points per radiograph). In addition, the spine was modelled as an articulated structure to take advantage of the dependencies between adjacent vertebrae in terms of location, orientation and shape. The accuracy of the method was assessed for a total of 30 patients with mild to severe scoliosis (Cobb angle [22°, 70°]) by comparison with a previous validated method. Reconstruction time was 90 s for mild patients, and 110 s for severe. Results show an accuracy of ∼0.5mm locating vertebrae, while orientation accuracy was up to 1.5° for all except axial rotation (3.3° on moderate and 4.4° on severe cases). Clinical indices presented no significant differences to the reference method (Wilcoxon test, p ≤ 0.05) on patients with moderate scoliosis. Significant differences were found for two of the five indices (p=0.03) on the severe cases, while errors remain within the inter-observer variability of the reference method. Comparison with state-of-the-art methods shows that the method proposed here generally achieves superior accuracy while requiring less reconstruction time, making it especially appealing for clinical routine use.

Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources

The execution of scientific applications, under the utility computing model, is constrained to Quality of Service (QoS) parameters. Commonly, applications have time and cost constraints such that all tasks of an application need to be finished within a user-specified Deadline and Budget. Several algorithms have been proposed for multiple QoS workflow scheduling, but most of them use search-based strategies that generally have a high time complexity, making them less useful in realistic scenarios. In this paper, we present a heuristic scheduling algorithm with quadratic time complexity that considers two important constraints for QoS-based workflow scheduling, time and cost, named Deadline-Budget Constrained Scheduling (DBCS). From the deadline and budget defined by the user, the DBCS algorithm finds a feasible solution that accomplishes both constraints with a success rate similar to other state-of-the-art search-based algorithms in terms of the successful rate of feasible solutions, consuming in the worst case only approximately 4% of the time. The DBCS algorithm has a low-time complexity of O ( n 2 . p ) for n tasks and p processors. A review of multiple QoS parameter workflow scheduling.A new multiple QoS algorithm with quadratic complexity for workflow scheduling.Similar performances of search-based algorithms in a small fraction of the time.Results for randomly generated graphs as well as for real-world applications.

Fairness Resource Sharing for Dynamic Workflow Scheduling on Heterogeneous Systems

For most Heterogeneous Computing Systems (HCS) the completion time of an application is the most important requirement. Many applications are represented by a workflow that is therefore schedule in a HCS system. Recently, researchers have proposed algorithms for concurrent workflow scheduling in order to improve the execution time of several applications in a HCS system. Although, most of these algorithms were designed for static scheduling, that is all application must be submitted at the same time, there are a few algorithms, such as OWM (online workflow Management) and RANK_HYBD, that were presented for dealing with dynamic application scheduling. In this paper, we present a new algorithm for dynamic application scheduling. The algorithm focus on the Quality of Service (QoS) experienced by each application (or user). It reduces the waiting and execution times of each individual workflow, unlike other algorithms that give privilege to average completion time of all workflows. The simulation results show that the proposed approach significantly outperforms the other algorithms in terms of individual response time.

Apresentação de um Banco de Desenvolvimento e Ensaio para Objectos Deformáveis

Neste artigo sera apresentado um sistema, de analise e processamento de imagem e de computacao grafica, que tem vindo a ser desenvolvido no Laboratorio Sinal e Imagem do INEB ? Instituto de Engenharia Biomedica desde 1995. A opcao pelo desenvolvimento de uma nova aplicacao, em vez da adaptacao de uma aplicacao ja existente, deveu-se a constatacao, por parte dos seus autores, da falta de plataformas que fossem adequadas nao so para serem utilizadas como pacotes fechados, mas tambem como aplicacoes abertas que facilmente permitissem a incorporacao de novas funcoes. O desejo do sistema incluir funcoes comuns na area da analise e processamento de imagem, em conjunto com algumas ferramentas habitualmente existentes no dominio da computacao grafica, e a necessaria possibilidade de controlar comodamente as condicoes de ensaio e de analise dos resultados obtidos por novos algoritmos, foram tambem argumentos importantes que condicionaram a decisao do desenvolvimento de um novo sistema.

Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters

This paper addresses the problem of minimizing the scheduling length (make-span) of a batch of jobs with different arrival times. A job is described by a direct acyclic graph (DAG) of parallel tasks. The paper proposes a dynamic scheduling method that adapts the schedule when new jobs are submitted and that may change the processors assigned to a job during its execution. The scheduling method is divided into a scheduling strategy and a scheduling algorithm. We also propose an adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan. The results of a comparison of this algorithm with another DAG scheduler using a simulation of several machine configurations and job types shows that P-HEFT gives a shorter makespan for a single DAG but scores worse for multiple DAGs. Finally, the results of the dynamic scheduling of a batch of jobs using the proposed scheduler method showed significant improvements for more heavily loaded machines when compared to the alternative resource reservation approach.

Static scheduling of dependent parallel tasks on heterogeneous clusters

This paper addresses the problem of scheduling parallel tasks, represented by a direct acyclic graph (DAG) on heterogeneous clusters. Parallel tasks, also called malleable tasks, are tasks that can be executed on any number of processors with its execution time being a function of the number of processors allotted to it. The scheduling of independent parallel tasks on homogeneous machines has been extensively studied and the case of parallel tasks with precedence constraints has been studied for tree-like graphs. For arbitrary precedence graphs and for heterogeneous machines, the optimization problem is more complex because the processing time of a given task depends on the number of processors and on the total processing capacity of those processors. This paper presents a list scheduling algorithm to minimize the total length of the schedule (makespan) of a given set of parallel tasks, whose dependencies are represented by a DAG

Dynamic Job Scheduling on Heterogeneous Clusters

This paper addresses the problem of scheduling dynamicallymulti-user and independent jobs on clusters, both homogeneous and heterogeneous. The dynamic behaviormeans that the scheduler is able to adapt the schedulingwhen new jobs are submitted and also when processorsavailability changes. The scheduler has two main featurescomparing to other solutions: it considers a job as beingdescribed by a direct acyclic graph (DAG) and it is ableto schedule parallel tasks, when appropriate, instead of the common dynamic mapping approach that assigns an entirejob to a processor or a fixed set of processors. The scheduling method is divided in a scheduling strategy and a scheduler algorithm, so that other scheduling algorithms can be incorporated. In this paper two static DAG schedulers for heterogeneous machines are considered. The results show the behavior of the scheduling method for the short completion time of a batch of jobs. These results show better performance when compared to the common schedulers strategies that fix the number of processors per job or assign one processor per job.